In a gas-turbine-combined-cycle power plant, land boiler plant, wind power plant, etc., when an alarm call telling occurrence of abnormality in the equipment in the plant such as abnormal noise or abnormal measured value is emitted, it is necessary to guess or infer the cause of the abnormality and implement measures such as making repairs depending on the cause.
Therefore, when malfunction occurs in the equipment in the plant, generally an operator in the plant guesses the cause of the malfunction based on his or her own experience and past records in the plant and takes an action probable to solve the cause conventionally (conventional method 1).
In another conventional method (conventional method 2), when malfunction occurs in the equipment in the plant, the operator informs a maintenance expert of the plant maker of the condition of the malfunction, the expert of the plant maker guesses the cause of the malfunction based on the malfunction database of the plant maker and informs the operator of the result of inference, and the operator takes an action probable to solve the cause.
In the conventional method 1, presence of an operator having sufficiently specialized knowledge and skill is indispensable, however, it is difficult to acquire or train such an expert in short time, and it is possible to happen that a false action is taken when such an expert can not be secured and damage due to the malfunction increases.
In the conventional method 2, similarly presence of an expert of the plant maker having sufficiently specialized knowledge and skill is indispensable, however, it is difficult to secure or train such an expert in the plant maker in short time, and it is possible to happen that a false action is taken when such an expert can not be secured and damage due to the malfunction increases.
In patent literature 1 is disclosed a method which is adopted as an automatic diagnosing method in a printer not in equipment in a plant. According to the method, a Bayesian network is utilized to infer the cause of the malfunction probabilistically and actions probable to solve the cause of the malfunction are informed to the operator together with effect and cost of implementation of each of the actions.
However, with the method disclosed in the patent literature 1, although sufficiently specialized knowledge and skill of experts concerning printers are collected and inputted in the Bayesian network, information of sound and information concerning shape or pattern of malfunction, which is difficult to be quantified but valuable for inferring causes of malfunction in printers are not adopted in the network, and there is a possibility that the result of inference is not sufficient in accuracy.
In the case of large-scale equipment used in a plant, it is necessary to continue operation of the equipment for extended period of time as long as possible, because expenses pile up when once the operation of the equipment is stopped. Therefore, information for determining whether to bring the equipment to a halt to repair, or whether to continue the operation to the weekend and then to repair, or whether to continue the operation under restricted operation condition until next periodic overhaul, is important.
According to the method of the patent literature 1, concrete actions to take against malfunction can be taught, however, they are for coping with malfunction in a printer which is a small-scale equipment, and information concerning timing of halting operation of the equipment, which is important in large-scale equipment as mentioned above, can not be provided.
Further, although concrete actions against malfunction are taught, a method to infer another malfunction probable to occur after the action is taken due to deterioration and method to confirm the deterioration is not disclosed.
Therefore, the method disclosed in the patent literature 1 which is art of automatic diagnosis of printers can not be adopted for large-scale equipment used in plants.
The patent literature 1: Japanese Laid-Open Patent Application No. 2001-75808.